Researchers are using supercomputers to introduce and assess the impact of different configurations of defects on the performance of a superconductor. “When people think of targeted evolution, they might think of people who breed dogs or horses,” said Argonne materials scientist Andreas Glatz, the corresponding author of the study. ​“Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects.”

Registration is now open for the Forum Teratec in France. The event takes place June 11-12 in Palaiseau. “The Forum Teratec is the premier international meeting for all players in HPC, Simulation, Big Data and Machine Learning (AI). It is a unique place of exchange and sharing for professionals in the sector. Come and discover the innovations that will revolutionize practices in industry and in many other fields of activity.”

In this AI Podcast, Brant Robertson from UC Santa Cruz describes how astronomers are turning to AI to turn the vast quantities of data that will be pouring out of next-generation telescopes into world-changing scientific discoveries. “Good news: astronomers are getting new tools to let them see further, better than ever before. The bad news: they’ll soon be getting more data than humans can handle.”

In this special guest feature from Scientific Computing World, Gemma Church writes that the aerospace industry is stuck in the past – but it isn’t due to a lack of new simulation and modeling techniques. “While there have been incremental improvements, in terms of creating quieter and more fuel-efficient aeroplanes, there is not a lot of innovation coming in, compared to the last century when we created aeroplanes moving at the speed of sound and made air travel available to the masses.”

Today Stony Brook University launched the Institute for AI-Driven Discovery and Innovation to advance AI research and apply the transformative power of innovation driven by AI across disciplines. The AI Institute will focus on four grand challenges: health care; infrastructure; education; and, finance. It will also focus on five foundational research areas: automated and scalable knowledge acquisition; predictive intelligence; explainable AI; trustworthy AI; and, ethical AI.

Intel has launched a Model Zoo for Intel Architecture, an open-sourced collection of optimized machine learning inference applications that demonstrates how to get the best performance on Intel platforms. The project contains more than 20 pre-trained models, benchmarking scripts, best practice documents, and step-by-step tutorials for running deep learning (DL) models optimized for Intel Xeon Scalable processors.

Today Google announced that its Google Cloud TPU Pods are now publicly available in beta. Designed to help Machine Learning researchers iterate faster and train more capable machine learning models, TPU Pods can include more than 1,000 individual TPU chips connected by an ultra-fast, two-dimensional toroidal mesh network.

Hyperion Research will hold their annual HPC Market Update briefing at ISC 2019 in Frankfurt. “As Hyperion Research, we continue all the worldwide activities that spawned the world’s most respected HPC industry analyst group. For more than 25 years, we’ve helped IT professionals, business executives, and the investment community make fact-based decisions on technology purchases and business strategy.”

In this video from the HPC User Forum, Frank Alexander from Brookhaven National Laboratory presents: ExaLearn – ECP Co-Design Center for Machine Learning. “It is increasingly clear that advances in learning technologies have profound societal implications and that continued U.S. economic leadership requires a focused effort, both to increase the performance of those technologies and to expand their applications. Linking exascale computing and learning technologies represents a timely opportunity to address those goals.”

“InstaDeep offers a pioneering AI as a Service solution enabling organizations of any size to leverage the benefits of AI and Machine Learning without the time, costs and expertise required to run their own AI stacks. Excelero’s NVMesh, in turn, allows InstaDeep to access the low-latency, high-bandwidth performance that is essential for running customer AI and ML workloads efficiently – and gain the scalability vital to InstaDeep’s own rapid growth.”

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Industry Perspectives

Often, it’s not enough to parallelize and vectorize an application to get the best performance. You also need to take a deep dive into how the application is accessing memory to find and eliminate bottlenecks in the code that could ultimately be limiting performance. Intel Advisor, a component of both Intel Parallel Studio XE and Intel System Studio, can help you identify and diagnose memory performance issues, and suggest strategies to improve the efficiency of your code. [READ MORE…]

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